{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2d3bd3f-9493-48db-9ee5-7fa049f413af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as sns  \n",
    "  \n",
    "# 第一个图表的设置和数据  \n",
    "np.random.seed(0)  \n",
    "spins1 = 2 * np.random.randint(0, 2, size=(5, 5)) - 1  \n",
    "  \n",
    "# 第二个图表的函数和设置  \n",
    "J = -1  # 相邻自旋间的相互作用强度  \n",
    "  \n",
    "def calculate_energy(spin_matrix, h):  \n",
    "    \"\"\"计算二维伊辛模型在给定外磁场下的能量\"\"\"  \n",
    "    L = len(spin_matrix)  \n",
    "    energy = 0  \n",
    "    for i in range(L):  \n",
    "        for j in range(L):  \n",
    "            neighbors = [  \n",
    "                spin_matrix[(i+1) % L, j],  # 上  \n",
    "                spin_matrix[(i-1) % L, j],  # 下  \n",
    "                spin_matrix[i, (j+1) % L],  # 右  \n",
    "                spin_matrix[i, (j-1) % L]   # 左  \n",
    "            ]  \n",
    "            energy -= J * spin_matrix[i, j] * sum(neighbors)  \n",
    "            energy -= h * spin_matrix[i, j]  \n",
    "    return energy / 2  \n",
    "  \n",
    "def plot_energies_together(ax, h_values):  \n",
    "    sns.set(style=\"whitegrid\")  \n",
    "    plt.rcParams.update({  \n",
    "        'font.size': 20,  # 调整为更适合子图的大小  \n",
    "        'axes.titlesize': 20,  \n",
    "        'axes.labelsize': 25,  \n",
    "        'xtick.labelsize': 25,  \n",
    "        'ytick.labelsize': 25,  \n",
    "        'legend.fontsize': 25,  \n",
    "        'lines.linewidth': 2.0  \n",
    "    })  \n",
    "  \n",
    "    for h in h_values:  \n",
    "        energies = []  \n",
    "        for _ in range(1000):  \n",
    "            spins = np.random.choice([-1, 1], size=(5, 5))  \n",
    "            energy = calculate_energy(spins, h)  \n",
    "            energies.append(energy)  \n",
    "  \n",
    "        sorted_energies = np.sort(energies)[::-1]  \n",
    "        x = np.arange(len(sorted_energies))  \n",
    "        y = sorted_energies  \n",
    "  \n",
    "        ax.step(x, y, where='mid', label=f'h = {h}', linewidth=3)  \n",
    "  \n",
    "    ax.set_xlabel(r'Spin Configurations')  \n",
    "    ax.set_ylabel(r'Energy')  \n",
    "    ax.legend()  \n",
    "    \n",
    "# 创建一行两列的子图  \n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9))  \n",
    "\n",
    "# 绘制第一个图  \n",
    "for i in range(6):  \n",
    "    ax1.axhline(i, color='k', lw=3)  \n",
    "    ax1.axvline(i, color='k', lw=3)  \n",
    "    \n",
    "for i in range(5):  \n",
    "    for j in range(5):  \n",
    "        if spins1[i, j] == 1:  \n",
    "            ax1.text(j+0.5, i+0.5, '↑', fontweight='bold', ha='center', va='center', fontsize=40, color='b')  \n",
    "        else:  \n",
    "            ax1.text(j+0.5, i+0.5, '↓', fontweight='bold', ha='center', va='center', fontsize=40, color='r')  \n",
    "            \n",
    "ax1.set_xticks([])  \n",
    "ax1.set_yticks([])  \n",
    "ax1.set_facecolor('0.9')  \n",
    "ax1.text(0.0, 1.05, '(A)', fontweight='bold', transform=ax1.transAxes, fontsize=25,  \n",
    "         verticalalignment='top', horizontalalignment='left')  \n",
    "  \n",
    "# 绘制第二个图  \n",
    "plot_energies_together(ax2, [0, 0.45])  \n",
    "ax2.text(0.0, 1.05, '(B)', fontweight='bold', transform=ax2.transAxes, fontsize=25,  \n",
    "         verticalalignment='top', horizontalalignment='left')  \n",
    "  \n",
    "# 调整布局并保存图像  \n",
    "plt.tight_layout() \n",
    "plt.savefig('ising_model.pdf', dpi=300, bbox_inches='tight')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21cd125e-1bc1-495c-8fdd-8765603024e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
